Popularity of Hearthstone Cards

The following packages are used:

library(dplyr)
library(ggplot2)
library(jsonlite)
library(knitr)
library(readr)
library(stringr)
library(tidyr)
library(zoo)

Introduction

Hearthstone is a popular collectible card game published by Blizzard Entertainment in 2014, which is based on the Warcraft series by the same company. The goal of the game is to build a deck of 30 cards and win by defeating the opponent’s or getting the opponent to concede first.

Cards can be classified according to the following categories:

• Class: Whether it can be used by only one, or many of the 9 classes in the game (Neutral)
• Rarity: Free, Common, Rare, Epic, Legendary
• Type: Minion, Spell, Weapon, Hero
• Set: Besides the Basic and Classic sets, additional cards are added to the game during expansions; the set also determines if it is eligible for Standard format.

Datasets Used

There are three datasets:

• data.csv contains a list of decks submitted by players to HearthPwn from 2013 (pre-launch) to 2017,
• refs.json contains detailed information about all cards (both collectible and non-collectible) up to March 2017.
  • These files are taken from the following Kaggle webpage: History of Hearthstone.
• cards_collectible.json contains detailed information about all the cards that are collectible in the game.
  • This file can be obtained from HearthstoneJSON.

Questions

1. Which are the most popular cards used in Ranked decks?

Breaking down the question

Which are the most popular cards used in Ranked decks?

We focus on the Ranked format where players get to decide which cards to include in their deck, therefore the cards’ popularity are more accurately represented, and the gameplay is not subject to additional constraints that other game modes (like Tavern Brawls and Adventures) may impose.

How do we determine popularity?

We determine popularity by the number of decks that include at least 1 copy in the starting 30 cards (not generated by other effects).

A deck can include at most 2 copies of any card (1 for Legendary cards), thus a card’s popularity is not heavily influenced by the number of copies players wish to use.

Possible biases to consider

Since Neutral cards can be used by multiple classes, they should be more popular than Class-specific cards.

For the Wild format, cards from the older sets may be more popular simply because they have been in the game longer.

For the Standard format, cards from the Basic and Classic sets will be more popular because they do not rotate out of the format unlike expansion cards.

Why address such a question?

If a certain card becomes too popular (i.e. the community thinks players must include it in their decks), it reduces the card variety in the metagame and makes gameplay frustrating for other players (amongst other consequences). In the long term, this may lead to player attrition and loss of potential revenue (when players purchase card packs or other cosmetics).

Historically, Blizzard has dealt with problematic cards in one of several ways:

• Introduce new cards that work well against them
• Adjust the card’s mechanics to weaken their power
• Shift a card into the Hall of Fame which removes it from Standard format.

Loading the data

data_path <- file.path("..", "data")

decks_raw <- read_csv(file.path(data_path, "data.csv"))
cards_raw <- fromJSON(file.path(data_path, "cards_collectible.json"), flatten = TRUE)

Decks data

The decks_raw data has 346232 rows and 41 columns. The first 11 columns describe the deck’s attributes (like date submitted, class, deck format) while the remaining 30 columns describe the cards each deck contains (based on the card’s unique ID which can be referenced from the cards_raw data).

Detailed information on the variables can be found on the Kaggle dataset.

names(decks_raw)

##  [1] "craft_cost"     "date"           "deck_archetype" "deck_class"    
##  [5] "deck_format"    "deck_id"        "deck_set"       "deck_type"     
##  [9] "rating"         "title"          "user"           "card_0"        
## [13] "card_1"         "card_2"         "card_3"         "card_4"        
## [17] "card_5"         "card_6"         "card_7"         "card_8"        
## [21] "card_9"         "card_10"        "card_11"        "card_12"       
## [25] "card_13"        "card_14"        "card_15"        "card_16"       
## [29] "card_17"        "card_18"        "card_19"        "card_20"       
## [33] "card_21"        "card_22"        "card_23"        "card_24"       
## [37] "card_25"        "card_26"        "card_27"        "card_28"       
## [41] "card_29"

# code is not run
glimpse(decks_raw) 

There are 8 rows that contain missing data. The missing values are in the 10th column, which contains the decks’ title as submitted by the users, so they can be safely ignored.

sum(!complete.cases(decks_raw))

## [1] 8

which(is.na(decks_raw), arr.ind = TRUE)

##         row col
## [1,]  16747  10
## [2,] 175608  10
## [3,] 216047  10
## [4,] 238021  10
## [5,] 278491  10
## [6,] 326192  10
## [7,] 329285  10
## [8,] 329286  10

Cards data

Additional information on the variables can be found on HearthstoneJSON.

There are two identifier fields for the cards: a character/string id and an integer dbfId. The decks_raw dataset uses the integer IDs to reference cards used.

dim(cards_raw)

## [1] 1751   65

# code is not run
names(cards_raw)

# code is not run
glimpse(cards_raw)

The following computes the number of missing values in each field, with the exception of those that are present as lists or data frames (mechanics, referencedTags, classes, entourage)

# code is not run
sapply(cards_raw, function(x) sum(is.na(x)))

Pre-processing

Decks data

The raw dataset is split into two, one containing the deck attributes and the other containing the deck composition (cards), with deck_id acting as the unique identifier. We also exclude decks created before launch (there were many card changes in the alpha and beta stages, making card popularity very volatile).

The decks_comp data will be pivoted to long format later on, thus excluding fields that are not related to the cards will minimize the size of the dataset.

launch_date <- as.Date("2014-03-11")

decks_attr <- decks_raw %>% 
    filter(date >= launch_date) %>% 
    select(deck_id, craft_cost:deck_format, deck_set:user)

decks_comp <- decks_raw %>% 
    filter(date >= launch_date) %>% 
    select(deck_id, card_0:card_29)

Within decks_attr, the factor/enumerated columns are identified and recast accordingly.

fct_cols_attr <- c("deck_archetype", "deck_class", "deck_format", "deck_set", "deck_type")

decks_attr[fct_cols_attr] <- lapply(decks_attr[fct_cols_attr], factor)
rm(fct_cols_attr)

Years and Months

While each deck has a submission date, we may also be interested in grouping the decks by month (which corresponds to Ranked seasons) and by year (which is marked by expansion release dates instead of calendar dates).

Years in the game based on a time period that:

• Starts with the release of the first card set of each year, which usually falls around April.
• Ends with the release of the first card set the next year (non-inclusive).

So based on the release dates, the years would be:

• 2014-03-11 to 2015-04-01 (Live, Naxxramas, Goblin vs Gnomes)
• 2015-04-02 to 2016-04-25 (Blackrock, Grand Tournament, League of Explorers)
• 2016-04-26 to 2017-03-19 (Old Gods, Karazhan, Gadgetzan)

# month (with year)
decks_attr$hsmonth <- as.yearmon(decks_attr$date)

# year (not by calendar)
decks_attr$hsyear <- case_when(
        decks_attr$date <= as.Date("2015-04-01") ~ "2014",
        decks_attr$date >= as.Date("2015-04-02") & 
            decks_attr$date <= as.Date("2016-04-25") ~ "2015",
        decks_attr$date >= as.Date("2016-04-26") ~ "2016"
    ) %>% 
        factor()

Deck Format

The Standard and Wild formats were formally introduced into the game on 2016-04-26 with the release of Whispers of the Old Gods; however the graphic below shows that many decks from June 2014 to April 2016 were marked as Wild.

We can simply relabel all decks created before 2016-04-26 as Standard since all cards before then are not separated by format:

decks_attr$deck_format[decks_attr$date < as.Date("2016-04-26")] <- "S"

A summary of the processed data is shown below:

##     deck_id         craft_cost         date           
##  Min.   : 36923   Min.   :    0   Min.   :2014-03-11  
##  1st Qu.:253573   1st Qu.: 2840   1st Qu.:2015-05-26  
##  Median :428597   Median : 5120   Median :2016-02-09  
##  Mean   :419989   Mean   : 5745   Mean   :2015-12-21  
##  3rd Qu.:603508   3rd Qu.: 7840   3rd Qu.:2016-08-09  
##  Max.   :749548   Max.   :48000   Max.   :2017-03-19  
##                                                       
##          deck_archetype     deck_class    deck_format
##  Unknown        :220501   Mage   :42230   S:307743   
##  Midrange Shaman:  5472   Priest :41756   W: 16361   
##  Control Priest :  5135   Paladin:39368              
##  Control Warrior:  4939   Warlock:35598              
##  Tempo Mage     :  4545   Druid  :35488              
##  Midrange Hunter:  4371   Shaman :33969              
##  (Other)        : 79141   (Other):95695              
##              deck_set              deck_type          rating        
##  Explorers       : 57307   Arena        :  8178   Min.   :   0.000  
##  Old Gods        : 49895   None         : 75120   1st Qu.:   1.000  
##  Blackrock Launch: 38900   PvE Adventure:  9059   Median :   1.000  
##  Gadgetzan       : 31329   Ranked Deck  :202104   Mean   :   2.777  
##  Naxx Launch     : 22283   Tavern Brawl :  6360   3rd Qu.:   1.000  
##  Yogg Nerf       : 22175   Theorycraft  : 19686   Max.   :4016.000  
##  (Other)         :102215   Tournament   :  3597                     
##     title               user              hsmonth      hsyear      
##  Length:324104      Length:324104      Min.   :2014   2014: 65119  
##  Class :character   Class :character   1st Qu.:2015   2015:128062  
##  Mode  :character   Mode  :character   Median :2016   2016:130923  
##                                        Mean   :2016                
##                                        3rd Qu.:2017                
##                                        Max.   :2017                
## 

Cards data

Many columns in the cards_raw data pertain to the card’s stats, mechanics and play requirements, which is better explained by the text on the card image. So we choose to only include the columns that we consider are core properties of the card (and not sufficiently explained by the card text):

cards_simple <- select(cards_raw,
                       dbfId,  name, cost,   cardClass, 
                       rarity, type,  set, collectible, id)

## Observations: 1,751
## Variables: 9
## $ dbfId       <int> 2539, 2541, 2545, 2572, 2542, 2549, 2571, 2544, 25...
## $ name        <chr> "Flame Lance", "Effigy", "Fallen Hero", "Arcane Bl...
## $ cost        <int> 5, 3, 2, 1, 3, 4, 3, 6, 8, 5, 4, 4, 1, 3, 2, 2, 4,...
## $ cardClass   <chr> "MAGE", "MAGE", "MAGE", "MAGE", "MAGE", "MAGE", "M...
## $ rarity      <chr> "COMMON", "RARE", "RARE", "EPIC", "RARE", "COMMON"...
## $ type        <chr> "SPELL", "SPELL", "MINION", "SPELL", "SPELL", "MIN...
## $ set         <chr> "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "TGT", "...
## $ collectible <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR...
## $ id          <chr> "AT_001", "AT_002", "AT_003", "AT_004", "AT_005", ...

The set column contains abbreviated names or nicknames and is not necessarily informative; we create a new column that uses the actual names of the card sets:

unique(cards_simple$set)

##  [1] "TGT"          "BOOMSDAY"     "BRM"          "GANGS"       
##  [5] "CORE"         "EXPERT1"      "HOF"          "NAXX"        
##  [9] "GILNEAS"      "GVG"          "HERO_SKINS"   "ICECROWN"    
## [13] "KARA"         "LOE"          "LOOTAPALOOZA" "OG"          
## [17] "UNGORO"

# list for recoding card sets
cardset_lst <- list(
    "CORE" = "Basic",
    "EXPERT1" = "Classic",
    # 2014 sets
    "NAXX" = "Curse of Naxxramas",
    "GVG" = "Goblins vs Gnomes",
    # 2015 sets
    "BRM" = "Blackrock Mountain",
    "TGT" = "The Grand Tournament",
    "LOE" = "League of Explorers",
    # 2016 sets
    "OG" = "Whispers of the Old Gods",
    "KARA" = "One Night in Karazhan",
    "GANGS" = "Mean Streets of Gadgetzan",
    "HOF" = "Hall of Fame",
    # 2017 sets
    "UNGORO" = "Journey to Un'Goro",
    "ICECROWN" = "Knights of the Frozen Throne",
    "LOOTAPALOOZA" = "Kobolds & Catacombs",
    # 2018 sets
    "GILNEAS" = "The Witchwood",
    "BOOMSDAY" = "The Boomsday Project"
)

cards_simple$card_set <- recode(cards_simple$set, !!!cardset_lst,
                                .default = "Other")

# drop the set column to avoid confusion (it remains in the cards_raw dataset)
cards_simple$set <- NULL
# remove list (no longer needed)
rm(cardset_lst)

Some columns are entirely uppercase, which we convert to title case for readability:

titlecase_cols <- c("cardClass", "rarity", "type")
cards_simple[titlecase_cols] <- lapply(cards_simple[titlecase_cols], 
                                       str_to_title)
rm(titlecase_cols)

The factor/enumerated columns are then identified and recast accordingly.

fct_cols_cards <- c("cardClass", "rarity", "type", "card_set")

cards_simple[fct_cols_cards] <- lapply(cards_simple[fct_cols_cards], factor)
rm(fct_cols_cards)

A summary of the processed data is shown below:

##      dbfId           name                cost          cardClass  
##  Min.   :    7   Length:1751        Min.   : 0.000   Neutral:657  
##  1st Qu.: 1987   Class :character   1st Qu.: 2.000   Paladin:123  
##  Median :38957   Mode  :character   Median : 4.000   Hunter :122  
##  Mean   :25375                      Mean   : 3.856   Mage   :122  
##  3rd Qu.:43163                      3rd Qu.: 5.000   Warlock:122  
##  Max.   :53187                      Max.   :20.000   Druid  :121  
##                                     NA's   :22       (Other):484  
##        rarity        type      collectible         id           
##  Common   :612   Hero  :  33   Mode:logical   Length:1751       
##  Epic     :298   Minion:1192   TRUE:1751      Class :character  
##  Free     :142   Spell : 471                  Mode  :character  
##  Legendary:253   Weapon:  55                                    
##  Rare     :446                                                  
##                                                                 
##                                                                 
##                          card_set  
##  Classic                     :236  
##  Basic                       :142  
##  Journey to Un'Goro          :135  
##  Knights of the Frozen Throne:135  
##  Kobolds & Catacombs         :135  
##  The Boomsday Project        :135  
##  (Other)                     :833

Mislabelled cards

As the decks are generated by human input, and there are multiple cards with the same name, it is recommended to check for cards that have the same name but wrong ID.

Specifically, we are looking for the version of each card that is collectible (since all cards used in Ranked decks must be collectible). This step requires loading the full card data (which contains non-collectible cards).

# IDs of all cards used
cards_used  <- decks_comp %>%
        select(card_0:card_29) %>%
        unlist(use.names = FALSE) %>%  # flatten into a vector
        unique() %>%
        sort()

# IDs of missing cards (using dbfId)
missing_cards <- cards_used[!cards_used %in% cards_simple$dbfId]

# how many cards are not found in the collectible cards dataset?
length(missing_cards)

## [1] 15

# data for all cards
cards_all_raw <- fromJSON(file.path(data_path, "refs.json"), flatten = TRUE)

# filter for those missing cards only
mssng_cards <- cards_all_raw %>% 
    select(dbfId, name, cardClass, type, collectible) %>% 
    filter(dbfId %in% missing_cards)

To find the correct IDs, we join them by name to the cards_simple. The dbfID.x on the left would be replaced by the dbfID.y on the right:

mislabelled <- mssng_cards %>% 
    select(dbfId, name) %>% 
    # also drops uncollectible mislabelled cards (which are not in simple_cards)
    inner_join(cards_simple, by = "name") %>% 
    arrange(name)
mislabelled

dbfId.x	name	dbfId.y	cost	cardClass	rarity	type	collectible	id	card_set
40341	Cleave	940	2	Warrior	Free	Spell	TRUE	CS2_114	Basic
2177	Dark Wispers	2009	6	Druid	Epic	Spell	TRUE	GVG_041	Goblins vs Gnomes
42146	Doppelgangster	40953	5	Neutral	Rare	Minion	TRUE	CFM_668	Mean Streets of Gadgetzan
38319	Druid of the Claw	692	5	Druid	Common	Minion	TRUE	EX1_165	Classic
2230	Druid of the Fang	2048	5	Druid	Common	Minion	TRUE	GVG_080	Goblins vs Gnomes
2310	Druid of the Flame	2292	3	Druid	Common	Minion	TRUE	BRM_010	Blackrock Mountain
40402	Evolve	38266	1	Shaman	Rare	Spell	TRUE	OG_027	Whispers of the Old Gods
41409	Jade Idol	40372	1	Druid	Rare	Spell	TRUE	CFM_602	Mean Streets of Gadgetzan
468	Mark of Nature	151	3	Druid	Common	Spell	TRUE	EX1_155	Classic
41609	Nefarian	2261	9	Neutral	Legendary	Minion	TRUE	BRM_030	Blackrock Mountain
38113	Raven Idol	13335	1	Druid	Common	Spell	TRUE	LOE_115	League of Explorers
1161	Starfall	86	5	Druid	Rare	Spell	TRUE	NEW1_007	Classic
38710	Unstable Portal	1929	2	Mage	Rare	Spell	TRUE	GVG_003	Goblins vs Gnomes
38653	Wisp	179	0	Neutral	Common	Minion	TRUE	CS2_231	Classic
137	Wrath	836	2	Druid	Common	Spell	TRUE	EX1_154	Classic

We create a named list that can be used within recode():

# list values are correct ids
mislab_recode <- as.list(mislabelled$dbfId.y)
# list names are mislabelled ids
names(mislab_recode) <- mislabelled$dbfId.x

# intermediate objects no longer needed
rm(cards_used, missing_cards, cards_all_raw, mssng_cards)

Other objects

The following items may be used for plotting:

# class colours
class_colors <- c(
    "Druid" = "#FF7D0A",
    "Hunter" = "#228B22", #"#ABD473",
    "Mage" = "#40C7EB",
    "Paladin" = "#F58CBA",
    "Priest" = "#FFFFFF",
    "Rogue" = "#FFF569",
    "Shaman" = "#0070DE",
    "Warlock" = "#8787ED",
    "Warrior" = "#C79C6E",
    "Neutral" = "#777777"
)

# rarity colours
rarity_colors <- c(
    "Free" = NA,
    "Common" = "#000000",
    "Rare" = "#1E90FF",
    "Epic" = "#9932CC",
    "Legendary" = "#FFB90F"
)

# release dates of card sets
release_dates <- c(
    "Launch" = "2014-03-11", 
    "Naxxramas" = "2014-07-22", 
    "GvG" = "2014-12-08",
    "Blackrock" = "2015-04-02",
    "TGT" = "2015-08-24",
    "Explorers" = "2015-11-12",
    "Old Gods" = "2016-04-26",
    "Karazhan" = "2016-08-11",
    "Gadgetzan" = "2016-12-01"
)

# darker plot theme for this doc
theme_doc <- theme(panel.spacing = unit(1, "points"),
                   panel.grid.major.y = element_blank(), 
                   plot.title = element_text(size = 12),
                   axis.text.y = element_text(size = 9),
                   panel.background = element_rect(fill = "#C3C3C3"))

Analysis of Popular Cards

The question we are trying to answer is

Which are the most popular cards used in Ranked decks (Standard format)?

We measure card popularity by counting the number of decks that include at least one copy of that card.

Preparation and Validation

To begin, we filter only for Ranked Decks in Standard format, pick out the variables of interest, join the decks to their compositions, and pivot the card columns.

ranked_deck_cards <- decks_attr %>%
    filter(deck_type == "Ranked Deck",
           deck_format == "S") %>%
    select(deck_id, deck_class, 
           date, hsmonth, hsyear, 
           deck_format, deck_set, craft_cost) %>% 
    
    inner_join(decks_comp, by = "deck_id") %>% 
    gather(card_no, card_id, card_0:card_29) %>% 
    select(-card_no) %>% # drop the key column which is unnecessary
    
    # select unique combinations of deck and card (ignore duplicates)
    distinct(deck_id, card_id, .keep_all = TRUE)

glimpse(ranked_deck_cards)

## Observations: 3,806,924
## Variables: 9
## $ deck_id     <int> 433003, 432997, 432995, 432992, 432990, 432989, 43...
## $ deck_class  <fct> Warrior, Paladin, Warrior, Priest, Mage, Mage, Rog...
## $ date        <date> 2016-02-19, 2016-02-19, 2016-02-19, 2016-02-19, 2...
## $ hsmonth     <S3: yearmon> Feb 2016, Feb 2016, Feb 2016, Feb 2016, Fe...
## $ hsyear      <fct> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 20...
## $ deck_format <fct> S, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,...
## $ deck_set    <fct> Explorers, Explorers, Explorers, Explorers, Explor...
## $ craft_cost  <int> 9840, 7700, 5740, 8780, 2800, 0, 2980, 1540, 4440,...
## $ card_id     <int> 75, 476, 75, 272, 113, 77, 251, 242, 290, 180, 476...

Before any further analysis, we want to check how many cards in this dataset are mislabelled:

sum(!(ranked_deck_cards$card_id) %in% cards_simple$dbfId)

## [1] 61269

We use the list mislab_recode to relabel those cards:

ranked_deck_cards$card_id <- recode(ranked_deck_cards$card_id, !!!mislab_recode)

# check again
sum(!(ranked_deck_cards$card_id) %in% cards_simple$dbfId)

## [1] 0

Popular Cards of All Time

To derive overall card popularity, we just count the number of times each card_id appears, then join it to the cards_simple data to retrieve the card details. After that, we can sort by number of decks:

# create set of ranked standard decks for pct calculation
rs_decks_all <- decks_attr %>% 
    filter(deck_type == "Ranked Deck",
           deck_format == "S")

pop_cards_all <- ranked_deck_cards %>% 
    group_by(card_id) %>% 
    summarise(n_decks = n(),
              pct_all = n_decks * 100 / nrow(rs_decks_all)) %>% 
    inner_join(cards_simple, by = c("card_id" = "dbfId"))

As the table below shows, the most popular cards are expected to be Neutral class since they can be used by all nine classes in the game, as opposed to Class-specific cards:

Most Popular Cards (by number of decks)

Name	Class	Rarity	Card Set	No. of Decks	% of All Decks
Azure Drake	Neutral	Rare	Hall of Fame	62327	32.2
Sylvanas Windrunner	Neutral	Legendary	Hall of Fame	46499	24.0
Emperor Thaurissan	Neutral	Legendary	Blackrock Mountain	37614	19.4
Dr. Boom	Neutral	Legendary	Goblins vs Gnomes	37033	19.1
Sludge Belcher	Neutral	Rare	Curse of Naxxramas	34718	17.9
Defender of Argus	Neutral	Rare	Classic	31081	16.1
Ragnaros the Firelord	Neutral	Legendary	Hall of Fame	29656	15.3
Piloted Shredder	Neutral	Common	Goblins vs Gnomes	28434	14.7
Acolyte of Pain	Neutral	Common	Classic	28340	14.6
Bloodmage Thalnos	Neutral	Legendary	Classic	28227	14.6

Which abilities/properties makes these cards popular?

• Card draw - Having more cards in hand gives the player more options to try and get an advantage over the opponent.
• Deathrattle - Triggers desirable effects when the minion is killed.
• Enhancement - Improving the player’s other cards on the board/in hand.
• Taunt - Protects the hero and minions from attack damage.

As the Hall of Fame was only created in April 2017; the popularity of those three cards (Azure Drake, Sylvanas Windrunner, Ragnaros the Firelord) may have been one of the factors behind Blizzard’s decision to move them out of Standard format and into the Hall of Fame.

The plot below shows the relative popularity of all cards in the dataset:

Popular Class-Specific Cards

So we can follow up by asking:

Amongst class-specific cards, which is the most popular card for each class?

By tallying the total number of decks by class, we can later evaluate for each class, the percentage of decks which use the most popular class-specific cards. After filtering out the Neutral cards, we can look for the most popular class-specific cards:

rs_decks_class <- rs_decks_all %>% 
    group_by(deck_class) %>% 
    summarise(tot_decks_class = n())

pop_cards_class <- pop_cards_all %>% 
    filter(!is.na(cardClass), cardClass != "Neutral") %>% 
    select(cardClass, name, rarity, card_set, n_decks) %>% 
    inner_join(rs_decks_class, by = c("cardClass" = "deck_class")) %>% 
    mutate(pct_class = n_decks * 100 / tot_decks_class)

The most popular class-specific cards are as follows:

Most Popular Class-Specific Cards

Class	Name	Rarity	Card Set	No. of Decks	Total No. of Class Decks	% of Class Decks
Druid	Swipe	Free	Basic	18802	20163	93.3
Hunter	Animal Companion	Free	Basic	15772	18226	86.5
Mage	Frostbolt	Free	Basic	22374	23850	93.8
Paladin	Truesilver Champion	Free	Basic	20395	23742	85.9
Priest	Shadow Word: Death	Free	Basic	21863	24396	89.6
Rogue	Eviscerate	Common	Classic	17717	19058	93.0
Shaman	Hex	Free	Basic	16880	21099	80.0
Warlock	Power Overwhelming	Common	Hall of Fame	13400	21268	63.0
Warrior	Fiery War Axe	Free	Basic	20856	21776	95.8

Which abilities/properties makes the above cards popular?

• Targeted damage - Being able to remove problematic minions that are otherwise protected by Taunt, or when your own minions do not have the damage to kill it.
• Transformation/removal - Alternatively, simply removing such minions from the board might be more feasible.
• Weapons - Most minions cannot attack the turn they are played, but weapons enable the player to attack immediately.

Some of the other popular cards (in the following plot) also have effects like card draw or area of effect.

While Frostbolt appeared in the largest number of decks, many of the most popular cards were included in more than 90% of decks from their class. The only exception would be Power Overwhelimg (Warlock) which only appeared in 63% of Warlock decks.

As the table shows, cards from the Basic and Classic sets tend to be more popular because they do not rotate out of Standard format, and they have been around since the launch of Hearthstone.

The following plot shows the relative popularity of the top 3 cards for each class. Dashed lines indicate the total number of decks representing each class in the dataset:

## Warning: Removed 21 rows containing missing values (geom_point).

Popular Cards by Year

Our decks_attr data spans 3 years from March 2014 to March 2017. Recall that years are defined by release dates of card sets instead of calendar months:

• 2014-03-11 to 2015-04-01 (Live, Naxxramas, Goblin vs Gnomes)
• 2015-04-02 to 2016-04-25 (Blackrock, Grand Tournament, League of Explorers)
• 2016-04-26 to 2017-03-19 (Old Gods, Karazhan, Gadgetzan)

To derive card popularity, we count the number of times each card_id appears in each hsyear:

rs_decks_year <- rs_decks_all %>% 
    group_by(hsyear) %>% 
    summarise(tot_decks_year = n())

pop_cards_year <- ranked_deck_cards %>% 
    group_by(hsyear, card_id) %>% 
    summarise(n_decks = n()) %>% 
    inner_join(cards_simple, by = c("card_id" = "dbfId"))

The following table shows the top 3 cards for each year. As expected, Neutral cards are used more often than class-specific cards. At the same time, the most popular card in 2015 and 2016 (Dr. Boom and Azure Drake) were considered even more essential than the others, considering that they were included in more than 40% of decks submitted:

Top 3 Cards in the Years 2014, 2015, 2016

Year	Name	Rarity	Card Set	No. of Decks	Total No. of Decks	% of Decks
2014	Sludge Belcher	Rare	Curse of Naxxramas	10490	35791	29.3
2014	Defender of Argus	Rare	Classic	8832	35791	24.7
2014	Harvest Golem	Common	Classic	8814	35791	24.6
2015	Dr. Boom	Legendary	Goblins vs Gnomes	31770	73406	43.3
2015	Sludge Belcher	Rare	Curse of Naxxramas	24227	73406	33.0
2015	Piloted Shredder	Common	Goblins vs Gnomes	23333	73406	31.8
2016	Azure Drake	Rare	Hall of Fame	34079	84381	40.4
2016	Sylvanas Windrunner	Legendary	Hall of Fame	21198	84381	25.1
2016	Brann Bronzebeard	Legendary	League of Explorers	19699	84381	23.3

Which abilities/properties makes the above cards popular?

• Battlecry - Triggers an effect when the card is played. Thus, Brann Bronzebeard complements such cards very well.
• Deathrattle - Triggers an effect when the minion is killed.
• Taunt - Protects the hero and minions from attack damage.

Popular Cards by Month

On the other hand, the Ranked ladder resets to mark a new Season at the start of every calendar month. Players take this opportunity to improve upon their previous ranking, and they may try to include new cards to overcome the popular decks that they tend to encounter.

From the graph above, there is usually a spike in the number of decks submitted when a new card set is released (marked with dark lines), which gradually tapers off until the next card set is released.

A possible question to ask may be:

Are there any shifts in card popularity in the three months following the release of the Old Gods card set (2016-04-26)?

Looking at the number of decks submitted every day for the first three months following 26th April in the plot below, the number of decks submitted (and therefore the number of cards included) sharply tapers off after the first week.

pop_cards_2016_aprjul <- ranked_deck_cards %>%
    filter(date >= as.Date("2016-04-26"),
           date <= as.Date("2016-07-25")) %>% 
    group_by(hsmonth, card_id) %>% 
    summarise(n_decks = n()) %>% 
    inner_join(cards_simple, by = c("card_id" = "dbfId"))

While cards from the latest card set were popular in the first week (still in April), cards from the older card sets (such as Classic) returned to the top spots in subsequent months. However, since the data concerns decks submitted (not necessarily played in games) we cannot infer that the new cards fell in popularity, only that decks submitted later favoured including older cards over the new ones.

Popular Cards in Apr-Jul 2016 following Old Gods release

Month	Name	Card Set	No. of Decks	Total No. of Decks	% of Decks
Apr 2016	C’Thun	Whispers of the Old Gods	2126	6721	31.6
Apr 2016	Azure Drake	Hall of Fame	2110	6721	31.4
Apr 2016	Sylvanas Windrunner	Hall of Fame	1984	6721	29.5
Apr 2016	Disciple of C’Thun	Whispers of the Old Gods	1866	6721	27.8
Apr 2016	Beckoner of Evil	Whispers of the Old Gods	1825	6721	27.2
May 2016	Sylvanas Windrunner	Hall of Fame	3742	10546	35.5
May 2016	Azure Drake	Hall of Fame	3333	10546	31.6
May 2016	Brann Bronzebeard	League of Explorers	2204	10546	20.9
May 2016	C’Thun	Whispers of the Old Gods	2188	10546	20.7
May 2016	Emperor Thaurissan	Blackrock Mountain	2132	10546	20.2
Jun 2016	Sylvanas Windrunner	Hall of Fame	1563	4776	32.7
Jun 2016	Azure Drake	Hall of Fame	1526	4776	32.0
Jun 2016	Emperor Thaurissan	Blackrock Mountain	979	4776	20.5
Jun 2016	Acolyte of Pain	Classic	927	4776	19.4
Jun 2016	Doomsayer	Classic	878	4776	18.4
Jul 2016	Azure Drake	Hall of Fame	1001	2858	35.0
Jul 2016	Sylvanas Windrunner	Hall of Fame	953	2858	33.3
Jul 2016	Emperor Thaurissan	Blackrock Mountain	638	2858	22.3
Jul 2016	Acolyte of Pain	Classic	599	2858	21.0
Jul 2016	Doomsayer	Classic	572	2858	20.0

Popular Cards by Card Set

Which were the most popular cards (Neutral and Class-specific) for each card set?

The first table shows the most popular Neutral card from each card set, with many of them being Legendary:

Most Popular Neutral Cards by Card Set

Card Set	Name	Class	Rarity	No. of Decks
Basic	Acidic Swamp Ooze	Neutral	Free	17107
Blackrock Mountain	Emperor Thaurissan	Neutral	Legendary	37614
Classic	Defender of Argus	Neutral	Rare	31081
Curse of Naxxramas	Sludge Belcher	Neutral	Rare	34718
Goblins vs Gnomes	Dr. Boom	Neutral	Legendary	37033
Hall of Fame	Azure Drake	Neutral	Rare	62327
League of Explorers	Brann Bronzebeard	Neutral	Legendary	25885
Mean Streets of Gadgetzan	Kazakus	Neutral	Legendary	5308
Mean Streets of Gadgetzan	Aya Blackpaw	Neutral	Legendary	5308
One Night in Karazhan	Barnes	Neutral	Legendary	9228
The Grand Tournament	Justicar Trueheart	Neutral	Legendary	15013
Whispers of the Old Gods	N’Zoth, the Corruptor	Neutral	Legendary	11764

The following plot shows the relative popularity of Neutral cards in each card set. Notably, the Basic, Blackrock Mountain and Karazhan sets have an overwhelming favourite (with a large difference over the 2nd most popular card):

## Warning: Removed 4 rows containing missing values (geom_point).

The next table shows the most popular Class-specific cards from each card set.

Most Popular Class Cards by Card Set

Card Set	Name	Class	Type	Rarity	No. of Decks
Basic	Frostbolt	Mage	Spell	Free	22374
Blackrock Mountain	Imp Gang Boss	Warlock	Minion	Common	11162
Classic	Eviscerate	Rogue	Spell	Common	17717
Curse of Naxxramas	Death’s Bite	Warrior	Weapon	Common	9012
Goblins vs Gnomes	Shielded Minibot	Paladin	Minion	Common	9620
Hall of Fame	Power Overwhelming	Warlock	Spell	Common	13400
League of Explorers	Entomb	Priest	Spell	Common	10527
Mean Streets of Gadgetzan	Dragonfire Potion	Priest	Spell	Epic	3410
One Night in Karazhan	Maelstrom Portal	Shaman	Spell	Rare	5505
The Grand Tournament	Totem Golem	Shaman	Minion	Common	10933
Whispers of the Old Gods	Thing from Below	Shaman	Minion	Rare	7361

The following plot shows the relative popularity of Class cards in each card set. Besides the Hall of Fame (which usually consists of Classic cards), the Naxxramas set has an overwhelming favourite in Death’s Bite (Warrior), which was highly valued both for its strong damage and Deathrattle effect.

## Warning: Removed 4 rows containing missing values (geom_point).

Summary and Reflection

So far, we have looked at cards that users tend to include in decks in Standard format for Ranked play, which is also used for official Hearthstone tournaments - making these popular cards highly visible to a wide audience. We have also looked at card popularity when broken down by various categories, such as class, time period and card set.

Are there any limitations to the data that may have affected our analysis?

The major limitation of this data is that it only looks at decks submitted to a third-party website, which brings up the following issues:

• Decks submitted may not necessarily be played in the game itself, either because other players think it is too weak, or because the user submits a joke deck that contains absurd combinations of cards and is not meant to be taken seriously.
• There is no data on how often decks and cards are actually played in the Ranked format.
• Likewise, there is no information on how effective the decks are at winning games. While the rating attribute may reflect how strong other players consider a deck, it is also biased towards the popularity of the user as well as the date of submission:
  • A deck may be initially strong and highly rated, but as new cards are introduced and old cards are removed from Standard format, the deck may wane in strength, but users are unlikely to retract their votes by this point in time.

How can we expand on this analysis?

• Examine popular combination of cards that complement each other well.
• Examine whether the crafting cost (in dust) of decks has any relation to its popularity (rating).